--- title: README emoji: ❤️ colorFrom: red colorTo: red sdk: static pinned: false --- SentenceTransformers 🤗 is a Python framework for using and training state-of-the-art embedding and reranker models. It can be used to compute embeddings from text, images, audio, or video using Sentence Transformer models ([quickstart](https://sbert.net/docs/quickstart.html#sentence-transformer)), to calculate similarity scores using Cross-Encoder (a.k.a. reranker) models ([quickstart](https://sbert.net/docs/quickstart.html#cross-encoder)), or to generate sparse embeddings using Sparse Encoder models ([quickstart](https://sbert.net/docs/quickstart.html#sparse-encoder)). Install the [Sentence Transformers](https://sbert.net/docs/installation.html) library. ``` pip install -U sentence-transformers ``` The usage is as simple as: ```python from sentence_transformers import SentenceTransformer # 1. Load a pretrained Sentence Transformer model model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") # The sentences to encode sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium.", ] # 2. Calculate embeddings by calling model.encode() embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # 3. Calculate the embedding similarities similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.6660, 0.1046], # [0.6660, 1.0000, 0.1411], # [0.1046, 0.1411, 1.0000]]) ``` Hugging Face makes it easy to collaboratively build and showcase your [Sentence Transformers](https://www.sbert.net/) models! You can collaborate with your organization, upload and showcase your own models in your profile ❤️

Push your Sentence Transformers models to the Hub ❤️

Find all Sentence Transformers models on the 🤗 Hub

To upload your Sentence Transformers models to the Hugging Face Hub, log in with `huggingface-cli login` and use the [`push_to_hub`](https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method within the Sentence Transformers library. ```python from sentence_transformers import SentenceTransformer # Load or train a model model = SentenceTransformer(...) # Push to Hub model.push_to_hub("my_new_model") ``` ## Learn more **Training guides:** - [Training and Finetuning Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): end-to-end training of bi-encoder embedding models. - [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): training Cross Encoder models for the second stage of retrieve-and-rerank pipelines. - [Training and Finetuning Sparse Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sparse-encoder): training SPLADE and other sparse encoders. **Multimodal:** - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): using text, image, audio, and video models through a single API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal models, with a Visual Document Retrieval walkthrough. **Efficiency techniques:** - [🪆 Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. - [Train 400x faster Static Embedding Models with Sentence Transformers](https://huggingface.co/blog/static-embeddings): CPU-friendly embedding models without attention. - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors.